There is an increasing need for efficient and robust software to process, integrate, and offer insight across the diversity of population imaging efforts underway across the BRAIN Initiative and other projects. Advances in statistical learning offer a set of technologies that can address many research applications using the extensive and varied data being produced by the projects. This can transform how we analyze and integrate new data. We propose using Nobrainer, an open source Python library that leverages these new learning technologies, as a platform that greatly simplifies integrating deep learning into neuroimaging research. Using this library, we are building and distributing user-friendly and cloud enabled end-user applications for the neuroimaging community. In Aim 1, we provide neural network models. We will create robust, pre-trained neural networks for brain segmentation and time series processing using brain scans from over 65000 individuals. Once trained, these models can then be used as the basis for many other applications, especially in reducing time of processing. We will subsequently use these base networks to perform image processing, image correction, and quality control. In Aim 2, we address the ability to train on private datasets. We will use Bayesian neural network models, which support principled use of prior information. We will use these networks to help detect when the models are expected to fail on an input, and provide visualizations to better understand how the model is working. In Aim 3, we focus on the engineering needed to maintain the software infrastructure, improve efficiency, and increase the scalability of our training methods. Here, we will extend, maintain, and disseminate Nobrainer, our open source software framework, together with training materials and ready to use, cloud-friendly, applications. We will also create much faster, neural network equivalents of time consuming image processing tasks (e.g., registration, segmentation, and annotation). The Nobrainer tools developed through these aims will allow users to find and apply the most pertinent applications and developers to extend the framework to support new architectures and disseminate new models and applications. We expect these tools to be used by any neuroimaging researcher through integration with BRAIN archives and popular software packages. These tools will significantly reduce data processing and new model development time, thus allowing faster exploration of hypotheses using public data and increase reusability of data through greater trust in model outputs.